class_docstr: MNIST data set from http://yann.lecun.com/exdb/mnist/
  
  Normalize defaults to true and scales the data 0 to 1.
  Size defaults to 28 for 28x28 pixels, specifying a smaller values allows
  cropping to a smaller size.
code: def gen_iterators(self):
      (X_train, y_train), (X_test, y_test), nclass = self.load_data()
      train = ArrayIterator(
          X_train,
          y_train,
          nclass=nclass,
          lshape=(1, self.size, self.size),
          name="train",
      )
      val = ArrayIterator(
          X_test,
          y_test,
          nclass=nclass,
          lshape=(1, self.size, self.size),
          name="valid",
      )
      self._data_dict = {"train": train, "valid": val}
      return self._data_dict
  
imports: from __future__ import division
  from future import standard_library
  from builtins import str
  import gzip
  import logging
  import numpy
  import os
  import tarfile
  from neon.util.compat import pickle
  from neon.util.compat import pickle_load
  from neon.data.datasets import Dataset
  from neon.data.dataiterator import ArrayIterator
label_desc: Use image node_id 3 for calling the MNIST obj's underlying member method code with example usage: obj.gen_iterators() and returns: self